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design and implement technical systems that support our data science and machine learning initiatives, software applications, and science gateways, bridging research concepts with practical, scalable
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the specialized subareas of machine learning, software engineering, and systems (databases, networking, etc.). An understanding of digital forensics or network forensics is a plus. Ability to work with a minimal
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field or method, including, but not limited to, numerical methods, machine learning, or parallel and distributed computing. Expertise in a parallelization method (e.g., CUDA or ROCm, MPI, OpenMP
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